Integration of wavelet network and image processing for determination of total pigments in bitter orange (Citrus aurantium L.) peel during ripening

This article presents a novel computer-aided prediction system based on integration of fixed-grid wavelet network (FGWN) and image processing technique for quantification of pigments (chlorophyll and carotenoid contents) in bitter orange peel. The proposed intelligent system captures the fruits image and uses image processing followed by principal component analysis (PCA) in order to extract the effective features. They consisted of 34 color features in the RGB and CIE color spaces, including some statistical parameters of color matrices (R, G, and B) or their differences and ratios as well as three CIE parameters. The first four principal components of the selected features are then fed as the inputs to the FGWN for estimating each pigment. The chlorophyll and carotenoid contents were modeled with the acceptable determination coefficient of 0.96 and 0.87, respectively. To evaluate the performance of the WN, it is compared against two common and powerful artificial intelligence techniques namely artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). The results revealed that the WN could provide almost the same performance with a much lower number of parameters. Therefore, this study develops a novel attempt of strategy to be used for quality control of the bitter orange peel. Practical applications A research challenge could be developing a fast, cheap, and nondestructive system that predicts pigments contents of citrus fruits for different applications and/or specifying the necessity of postharvest artificial de-greening to satisfy market demand. The integration of image processing and fixed-grid wavelet network (FGWN) is a potential approach to overcome the mentioned challenge. Such an integration showed acceptable results for the determination of chlorophylls and carotenoids content in bitter oranges in the current study. In terms of applicability, the parsimonious models of the FGWN is quite desirable for online uses.